code: def predict_proba(self, X):
      self.module.eval()
      X = torch.as_tensor(X)
      dataset = torch.utils.data.TensorDataset(X)
      dataloader = torch.utils.data.DataLoader(dataset, batch_size=64)
      dataloader = self.accelerator.prepare(dataloader)
  
      probas = []
      with torch.no_grad():
          for source, *_ in dataloader:
              output = self.module(source)
              output = self.accelerator.gather_for_metrics(output)
              output = output.cpu().detach().numpy()
              probas.append(output)
  
      return np.vstack(probas)
  
file_path: skorch/examples/accelerate-multigpu/run-no-skorch.py
imports: import numpy
  import torch
  from accelerate import Accelerator
  from sklearn.datasets import make_classification
  from sklearn.model_selection import cross_validate
  from sklearn.base import BaseEstimator
  from torch import nn
label_desc: Use image node_id 3 for calling the Net obj's underlying member method code with example usage: obj.predict_proba(X) and returns: np